Lastly, datasets that measure a model's robustness to corruptions include ImageNet-C [1]}, which uses data augmentation strategies to generate noisy versions of ImageNet [2]} images.
We note that other data augmentation tools and pipelines have been used to generate perturbations and corruptions of in-distribution data, including [3]}, [4]}, [5]}, [6]} for image and text data.
In this paper, we present new evaluation data for benchmarking both of the out-of-distribution tasks described in this section.
Our new benchmark data enables researchers to analyze document classifier performance on out-of-domain inputs as well as in-domain data from a "shifted" input distribution.
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